Duration
25h Th, 15h Pr, 10h Mon. WS
Number of credits
| Bachelor in mathematics | 6 crédits |
Lecturer
Language(s) of instruction
French language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
In this course, the classical methods of hypothesis testing will be developed in detail. Then, an introduction to some techniques used in multivariate statistics will be presented (i.e. Principal Component Analysis and clustering). If time allows, a short introduction to the modelling of multiple regression models will be added.
Learning outcomes of the learning unit
After this course, the students should be able to take decisions based on the result of statistical testing. Moreover, they should have acquired the basic approach of high dimensional data analysis.
Prerequisite knowledge and skills
Probability and descriptive and inferential statistics (estimation, confidence intervals)
Planned learning activities and teaching methods
The course consists of ex-cathedra lectures on the theory and there are alors 15 hours of practicals (exercises and data analyses on statistical softwares). Personal work (for ex: first manipulaitons of the softwares) completes the learning.
Mode of delivery (face to face, distance learning, hybrid learning)
The courses and the tutorials/practicals are given face-to-face over the first semester according to a timetable distributed to the students in the beginning of the academic year.
Organisational adjustments related to the current health context
If face-to-face teaching is no longer allowed, the theory will be exposed by means of videos and the practicals will be organised in a distance way via a virtual classroom on eCampus.
Concerning the exam, if it cannot take place in the rooms of the university, a distance version will be organised. It would consist of a part on theory organised via a virtual discussion on Collaborate and a part on exercises and data analyses (resolutions on paper and scripts of stoftware, photography and up-load on eCampus). All collaboration between students will be sanctionned.
Recommended or required readings
(Partial) Course notes, slides and exercises sheets will be made available along the year on eCampus.
Assessment methods and criteria
Below you will find information on the evaluation methods planned for in-person and remote exams as well as those planned for hybrid sessions. Depending on how the health crisis evolves, the chosen method will be communicated to you no later than one month before the start of the exam session.
Any session :
- In-person
written exam ( open-ended questions )
- Remote
written exam ( open-ended questions )
- If evaluation in "hybrid"
preferred in-person
Additional information:
The final mark is a weighted mean of the marks attributed to the two following assesments:
- written exam on theory and exercises (without access to personnal notes nor usage of software)
- written exam in the computer room for a data analysis
A grade inferior to 6/20 in any of the parts will automatically lead to a failed mark for the course.
Work placement(s)
Organizational remarks
Contacts
G. Haesbroeck (G.Haesbroeck@uliege.be)
S. Klenkenberg (S.Klenkenberg@uliege.be)